Biology subjects

Posani, L.

Publications and source records attributed to Posani, L..

12 recordsLinked to original sources

bioRxiv2026

A new neural code for position: generalizable spatial maps in mouse medial frontal cortex

Navigation requires the brain to encode spatial relationships that support extrapolation from explored to unexplored regions of space. We identified a novel neural code for space in the mouse medial frontal cortex (mFC) that is well suited for this form of spatial generalization. Using calcium imaging of excitatory neurons in mFC, hippocampus (dorsal CA1), and lateroventral orbitofrontal cortex (lvOFC) as mice explored a square arena with landmarks, we found that mFC activity supports extrapolation. Linear regression of the animal's x or y coordinates trained in one region of the arena generalized to a completely different region. This property was observed in mFC but not in dCA1 or lvOFC. A model in which neurons encode the squared distance from the animal to multiple landmarks explains several properties of the geometry of the mFC representation, including extrapolation. The neural map that we observed in the frontal cortex may provide a basis for vector computation in navigation and complex route planning.

neuroscience↗

bioRxiv2026

Decodanda: a Python toolbox for best-practice decoding and geometric analysis of neural representations

Neural decoding is a powerful approach for inferring which variables are represented in the activity of a population of neurons, with broad applications ranging from basic neuroscience to clinical settings such as brain-computer interfaces. More recently, decoding has also been used as a cross-validated tool for studying the computationally relevant properties of representational geometry, revealing not only whether a variable is encoded, but also how it is encoded and which computations the collective activity of neural populations may support. However, decoding analyses present several technical challenges and common pitfalls that can lead to misleading conclusions if not handled carefully. Here, we introduce Decodanda, a Python toolbox for decoding and geometric analysis of neural population activity. Decodanda provides functions for decoding arbitrary variables and for quantifying geometric features of neural representations, including shattering dimensionality and cross-condition generalization performance (CCGP). Importantly, the package automates several essential best-practice safeguards, including trial-based cross-validation to avoid training-testing leakage from temporally correlated neural traces (a particularly important issue for calcium imaging data), null models for statistical significance, pseudo-population pooling, and cross-variable balancing to determine which of a set of correlated variables is genuinely encoded in the activity. Decodanda is agnostic to the specific classifier used for decoding, and it is designed to be both user-friendly and highly customizable, allowing researchers to assemble flexible analysis pipelines from modular building blocks. Here, we provide an overview of the design principles of Decodanda and illustrate its use cases in neuroscience research. Documentation, example notebooks, and source code are available at github.com/lposani/decodanda.

neuroscience↗

bioRxiv2024

Impaired spatial coding and neuronal hyperactivity in the medial entorhinal cortex of APP NL-G-F mice

The progressive accumulation of amyloid beta (A{beta}) pathology in the brain has been associated with aberrant neuronal network activity and poor cognitive performance in preclinical mouse models of Alzheimers disease (AD). Presently, our understanding of the mechanisms driving pathology-associated neuronal dysfunction and impaired information processing in the brain remains incomplete. Here, we assessed the impact of advanced A{beta} pathology on spatial information processing in the medial entorhinal cortex (MEC) of 18-month App NL-G-F/NL- G-F knock-in (APP KI) mice as they explored contextually novel and familiar open field arenas in a two-day, four-session recording paradigm. We tracked single unit firing activity across all sessions and found that spatial information scores were decreased in MEC neurons from APP KI mice versus those in age-matched C57BL/6J controls. MEC single unit spatial representations were also impacted in APP KI mice. Border cell firing preferences were unstable across sessions and spatial periodicity in putative grid cells was disrupted. In contrast, MEC border cells and grid cells in Control mice were intact and stable across sessions. We then quantified the stability of MEC spatial maps across sessions by utilizing a metric based on the Earth Movers Distance (EMD). We found evidence for increased instability in spatially-tuned APP KI MEC neurons versus Controls when mice were re-exposed to familiar environments and exposed to a novel environment. Additionally, spatial decoding analysis of MEC single units revealed deficits in position and speed coding in APP KI mice in all session comparisons. Finally, MEC single unit analysis revealed a mild hyperactive phenotype in APP KI mice that appeared to be driven by narrow-spiking units (putative interneurons). These findings tie A{beta}-associated dysregulation in neuronal firing to disruptions in spatial information processing that may underlie certain cognitive deficits associated with AD.

neuroscience↗

bioRxiv2024

Rarely categorical, always high-dimensional: how the neural code changes along the cortical hierarchy

A long-standing debate in neuroscience concerns whether individual neurons are organized into functionally distinct populations that encode information differently ("categorical" representations [1-3]) and the implications for neural computation. Here, we systematically analyzed how cortical neurons encode cognitive, sensory, and movement variables across 43 cortical regions during a complex task (14,000+ units from the International Brain Laboratory public Brainwide Map data set [4]) and studied how these properties change across the sensory-cognitive cortical hierarchy [5]. We found that the structure of the neural code was scale-dependent: on a whole-cortex scale, neural selectivity was categorical and organized across regions in a way that reflected their anatomical connectivity. However, within individual regions, categorical representations were rare and limited to primary sensory areas, and neuronal responses were instead very diverse. With theoretical arguments and empirical evidence, we demonstrate that the diversity of neural responses enables high-dimensional representations and, hence, high separability, allowing linear readouts to separate experimental conditions in many arbitrary ways. Indeed, when accounting for information that is actually encoded in each area, all cortical regions exhibit maximal separability. Our results indicate that cortical circuits prioritize diversity over categorical structure, supporting a computational regime geared toward high-dimensional, highly-separable neural representations.

neuroscience↗

bioRxiv2024

Prefrontal representations of retrospective spatial working memory in a rodent radial maze task

Working memory is the cognitive capacity for temporarily holding information in mind for processing or use. It has been theorized to depend upon executive and mnemonic subcomponents, although the contextual mapping of these subcomponents is not complete. Perturbations of prefrontal cortex (PFC) delay activity disrupt spatial working memory performance in rodent tasks. However, recordings of unperturbed PFC delay activity do not consistently contain mnemonic representations of spatial information in these tasks, calling into question the role that mnemonic PFC representations play in freely-moving spatial working memory. We hypothesized that increasing task complexity might increase the likelihood of mnemonic PFC representation emergence. We therefore used an automated eight-arm radial maze to implement a novel match-to-sample rodent spatial working memory task with seven options on each trial, and recorded calcium activity in PFC neurons during task performance. We found that the delay-phase activity of PFC neurons indeed contained mnemonic representations of spatial information at the population level. These representations were retrospective rather than prospective, and--surprisingly--were more evident on error trials. Together with previous results, these observations suggest that in freely-moving spatial working memory tasks, PFC mnemonic representations emerge to empower deviation from a routine behavioral strategy. Significance StatementPrefrontal cortex (PFC) activity is necessary for optimal performance of freely-moving spatial working memory tasks in rodents. Despite this, PFC representations of retrospective actions or stimuli--one quintessential working memory hallmark--are only variably observed during task delays, complicating our understanding of the PFCs role in spatial working memory. Here, we examine cellular-resolution PFC activity in a high-optionality match-to-sample radial maze task and find retrospective delay representations. Strikingly, these delay representations are more evident in error trials. This suggests that in the freely-moving context, explicit PFC representations of retrospective information support deviations from an entrained behavioral strategy, rather than equally supporting all spatial working memory-based behavior.

animal behavior and cognition↗

bioRxiv2023

Minimal epistatic networks from integrated sequence and mutational protein data.

Predicting the functional effects of mutations to a wild-type protein sequence is a major computational challenge. We introduce here a computationally efficient procedure to identify the few, most informative epistatic links between residues in a protein, integrating sequence data and functional measurements with mutational scans. Our approach shows performances comparable to state-of-the-art deep networks, while requiring much less parameters and being hence much more interpretable. The selected network links mostly focus on the protein functional sites, adapt to the in vitro or in vivo function experimentally tested, and are not necessary related to structural contacts.

bioinformatics↗

bioRxiv2023

The representational geometry of emotional states in basolateral amygdala

Sensory stimuli associated with aversive outcomes cause multiple behavioral responses related to an animals evolving emotional state, but neural mechanisms underlying these processes remain unclear. Here aversive stimuli were presented to mice, eliciting two responses reflecting fear and flight to safety: tremble and ingress into a virtual burrow. Inactivation of basolateral amygdala (BLA) eliminated differential responses to aversive and neutral stimuli without eliminating responses themselves, suggesting BLA signals valence, not motor commands. However, two-photon imaging revealed that neurons typically exhibited mixed selectivity for stimulus identity, valence, tremble and/or ingress. Despite heterogeneous selectivity, BLA representational geometry was lower-dimensional when encoding valence, tremble and safety, enabling generalization of emotions across conditions. Further, tremble and valence coding directions were orthogonal, allowing linear readouts to specialize. Thus BLA representational geometry confers two computational properties that identify specialized neural circuits encoding variables describing emotional states: generalization across conditions, and readouts lacking interference from other readouts.

neuroscience↗

bioRxiv2022

Infer global, predict local: quantity-quality trade-off in protein fitness predictions from sequence data

Predicting the effects of mutations on protein function is an important issue in evolutionary biology and biomedical applications. Computational approaches, ranging from graphical models to deep-learning architectures, can capture the statistical properties of sequence data and predict the outcome of high-throughput mutagenesis experiments probing the fitness landscape around some wild-type protein. However, how the complexity of the models and the characteristics of the data combine to determine the predictive performance remains unclear. Here, based on a theoretical analysis of the prediction error, we propose descriptors of the sequence data, characterizing their quantity and quality relative to the model. Our theoretical framework identifies a trade-off between these two quantities, and determines the optimal subset of data for the prediction task, showing that simple models can outperform complex ones when inferred from adequately-selected sequences. We also show how repeated subsampling of the sequence data allows for assessing how much epistasis in the fitness landscape is not captured by the computational model. Our approach is illustrated on several protein families, as well as on in silico solvable protein models. Significance StatementIs more data always better? Or should one prefer fewer data, but of higher quality? Here, we investigate this question in the context of the prediction of fitness effects resulting from mutations to a wild-type protein. We show, based on theory and data analysis, that simple models trained on a small subset of carefully chosen sequence data can perform better than complex ones trained on all available data. Furthermore, we explain how comparing the simple local models obtained with different subsets of training data reveals how much of the epistatic interactions shaping the fitness landscape are left unmodeled.

biophysics↗

bioRxiv2022

The geometry of hippocampal CA2 representations enables abstract coding of social familiarity and identity

Social recognition consists of multiple memory processes, including the detection of familiarity - the ability to rapidly distinguish familiar from novel individuals - and recollection - the effortful recall of where a social episode occurred and who was present. At present, the neural mechanisms for these different social memory processes remain unknown. Here, we investigate the population code for novel and familiar individuals in mice using calcium imaging of neural activity in a region crucial for social memory, the dorsal CA2 area of the hippocampus. We report that familiarity changes CA2 representations of social encounters to meet the different demands of social memory. While novel individuals are represented in a low-dimensional geometry that allows for rapid generalization, familiar individuals are represented in a higher-dimensional geometry that supports high-capacity memory storage. The magnitude of the change in dimensionality of CA2 representations for a given individual predicts the performance of that individual in a social recognition memory test, suggesting a direct relationship between the representational geometry and memory-guided behavior. Finally, we show that familiarity is encoded as an abstract variable with neural responses generalizing across different identities and spatial locations. Thus, through the tuning of the geometry of structured neural activity, CA2 is able to meet the complex demands of multiple social memory processes.

neuroscience↗

bioRxiv2021

A synaptic novelty signal in the dentate gyrus supports switching hippocampal attractor networks from generalization to discrimination

Episodic memory formation and recall are complementary processes that put conflicting requirements on neuronal computations in the hippocampus. How this challenge is resolved in hippocampal circuits is unclear. To address this question, we obtained in vivo whole-cell patch-clamp recordings from dentate gyrus granule cells in head-fixed mice trained to explore and distinguish between familiar and novel virtual environments. We find that granule cells consistently show a small transient depolarization of their membrane potential upon transition to a novel environment. This synaptic novelty signal is sensitive to local application of atropine, indicating that it depends on metabotropic acetylcholine receptors. A computational model suggests that the observed transient synaptic response to novel environments may lead to a bias in the granule cell population activity, which can in turn drive the downstream attractor networks to a new state, thereby favoring the switch from generalization to discrimination when faced with novelty. Such a novelty-driven cholinergic switch may enable flexible encoding of new memories while preserving stable retrieval of familiar ones.

neuroscience↗

bioRxiv2019

The hippocampus as a perceptual map: neuronal and behavioral discrimination during memory encoding

The hippocampus is thought to encode similar events as distinct memory representations that are used for behavioral decisions. Where and how this "pattern separation" function is accomplished in the hippocampal circuit, and how it relates to behavior, is still unclear. Here we perform in vivo 2-photon Ca2+ imaging from hippocampal subregions of head-fixed mice performing a virtual-reality spatial discrimination task. We find that population activity in the input region of the hippocampus, the dentate gyrus, robustly discriminates small changes in environments, whereas spatial discrimination in CA1 reflects the behavioral performance of the animals and depends on the degree of differences between environments. Our results demonstrate that the dentate gyrus amplifies small differences in its inputs, while downstream hippocampal circuits will act as the final arbiter on this decorrelated information, thereby producing a "perceptual map" that will guide behaviour.

neuroscience↗

bioRxiv2018

Integration and multiplexing of positional and contextual information by the hippocampal network

The hippocampus is known to store cognitive representations, or maps, that encode both positional and contextual information, critical for episodic memories and functional behavior. How path integration and contextual cues are dynamically combined and processed by the hippocampus to maintain these representations accurate over time remains unclear. To answer this question, we propose a two-way data analysis and modeling approach to CA3 multi-electrode recordings of a moving rat submitted to rapid changes of contextual (light) cues, triggering back-and-forth instabitilies between two cognitive representations (Jezek et al, Nature 478, p 246 (2011)). We develop a dual neural activity decoder, capable of independently identifying the recalled cognitive map at high temporal resolution (comparable to theta cycle) and the position of the rodent given a map. Remarkably, position can be reconstructed at any time with an accuracy comparable to fixed-context periods, even during highly unstable periods. These findings provide evidence for the capability of the hippocampal neural activity to maintain an accurate encoding of spatial and contextual variables, while one of these variables undergoes rapid changes independently of the other. To explain this result we introduce an attractor neural network model for the hippocampal activity that process inputs from external cues and the path integrator. Our model allows us to make predictions on the frequency of the cognitive map instability, its duration, and the detailed nature of the place-cell population activity, which are validated by a further analysis of the data. Our work therefore sheds light on the mechanisms by which the hippocampal network achieves and updates multi-dimensional neural representations from various input streams.\n\nAuthor summaryAs an animal moves in space and receives external sensory inputs, it must dynamically maintain the representations of its position and environment at all times. How the hippocampus, the brain area crucial for spatial representations, achieves this task, and manages possible conflicts between different inputs remains unclear. We propose here a comprehensive attractor neural network-based model of the hippocampus and of its multiple input streams (including self-motion). We show that this model is capable of maintaining faithful representations of positional and contextual information, and resolves conflicts by adapting internal representations to match external cues. Model predictions are confirmed by the detailed analysis of hippocampal recordings of a rat submitted to quickly varying and conflicting contextual inputs.

neuroscience↗